[1] This paper evaluates the use of field data on the spatial variability of snow water equivalent (SWE) to guide the design of distributed snow models. An extensive reanalysis of results from previous field studies in different snow environments around the world is presented, followed by an analysis of field data on spatial variability of snow collected in the headwaters of the Jollie River basin, a rugged mountain catchment in the Southern Alps of New Zealand. In addition, area-averaged simulations of SWE based on different types of spatial discretization are evaluated. Spatial variability of SWE is shaped by a range of different processes that occur across a hierarchy of spatial scales. Spatial variability at the watershed-scale is shaped by variability in near-surface meteorological fields (e.g., elevation gradients in temperature) and, provided suitable meteorological data is available, can be explicitly resolved by spatial interpolation/extrapolation. On the other hand, spatial variability of SWE at the hillslope-scale is governed by processes such as drifting, sloughing of snow off steep slopes, trapping of snow by shrubs, and the nonuniform unloading of snow by the forest canopy, which are more difficult to resolve explicitly. Subgrid probability distributions are often capable of representing the aggregate-impact of unresolved processes at the hillslope-scale, though they may not adequately capture the effects of elevation gradients. While the best modeling strategy is case-specific, the analysis in this paper provides guidance on both the suitability of several common snow modeling approaches and on the choice of parameter values in subgrid probability distributions.

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Slope glaciers on Kilimanjaro (ca. 5000-6000 m MSL) reached their most recent maximum extent in the late nineteenth century (L19) and have receded since then. This study quantifies the climate signal behind the recession of Kersten Glacier, which generates information on climate change in the tropical midtroposphere between L19 and present. Multiyear meteorological measurements at 5873 m MSL serve to force and verify a spatially distributed model of the glacier's mass balance (the most direct link between glacier behavior and atmospheric forcing). At present the glacier is losing mass (522 6 105 kg m 22 yr 21 ), terminates at 5100 m, and the interannual variability of mass and energy budgets largely reflects variability in atmospheric moisture. Backward modeling of the L19 steady-state glacier extent (down to 4500 m) reveals higher precipitation (1160 to 1240 mm yr 21 ), higher air humidity, and increased fractional cloud cover in L19 but no significant changes in local air temperature, air pressure, and wind speed. The atmosphere in the simulated L19 climate transfers more energy to the glacier surface through atmospheric longwave radiation and turbulent heat-but this is almost entirely balanced by the decrease in absorbed solar radiation (due to both increased cloudiness and higher surface albedo). Thus, the energy-driven mass loss per unit area (sublimation plus meltwater runoff) was not appreciably different from today. Higher L19 precipitation rates therefore dominated the mass budget and produced a larger glacier extent in the past.

Meteorological and glaciological measurements obtained at 5873 m a.s.l. on Kersten Glacier, a slope glacier on the southern flanks of Kilimanjaro, are used to run a physically-based mass balance model for the period February 2005 to January 2006. This shows that net shortwave radiation is the most variable energy flux at the glacier-atmosphere interface, governed by surface albedo. The majority of the mass loss (∼65%) is due to sublimation (direct conversion of snow/ice to water vapour), with melting of secondary importance. Sensitivity experiments reveal that glacier mass balance is 2-4 times more sensitive to a 20% precipitation change than to a 1°C air temperature change. These figures also hold when the model is run with input data representative of a longer term mean period. Results suggest that a regional-scale moisture projection for the 21st century is crucial to a physically-based prediction of glacier retention on Africa's highest mountain.